IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911304912994304 |
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| author | Zheng, Jiachun Huang, Yunqing Yi, Nianyu |
| author_facet | Zheng, Jiachun Huang, Yunqing Yi, Nianyu |
| contents | In this work, we develop interface-gated physics-informed neural networks (IG-PINNs) to solve elliptic interface equations. In IG-PINNs, we use a fully connected neural network to capture the smooth behavior across the entire domain. In each subdomain separated by the interface, an interface-gated network is utilized to provide corrections at the interface. In the architectural design of the interface-gated network, we introduce a gating mechanism and a level-set function derived from the interface. This design enables the interface-gated network to effectively handle discontinuous jumps across the interface. Some numerical experiments have confirmed the effectiveness of the IG-PINNs, demonstrating higher accuracy compared with PINNs, interface PINNs (I-PINNs) and multi-domain PINNs (M-PINNs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems Zheng, Jiachun Huang, Yunqing Yi, Nianyu Numerical Analysis In this work, we develop interface-gated physics-informed neural networks (IG-PINNs) to solve elliptic interface equations. In IG-PINNs, we use a fully connected neural network to capture the smooth behavior across the entire domain. In each subdomain separated by the interface, an interface-gated network is utilized to provide corrections at the interface. In the architectural design of the interface-gated network, we introduce a gating mechanism and a level-set function derived from the interface. This design enables the interface-gated network to effectively handle discontinuous jumps across the interface. Some numerical experiments have confirmed the effectiveness of the IG-PINNs, demonstrating higher accuracy compared with PINNs, interface PINNs (I-PINNs) and multi-domain PINNs (M-PINNs). |
| title | IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2506.18332 |